# RINO: Renormalization Group Invariance with No Labels

arXiv: https://arxiv.org/abs/2509.07486 · HTML: https://mlsanity.com/paper/2509.07486/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 53 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [Spreading vectors for similarity search](https://mlsanity.com/paper/1806.03198/) (2018)
- [Particle Transformer for Jet Tagging](https://mlsanity.com/paper/2202.03772/) (2022)
- [Energy Flow Networks: Deep Sets for Particle Jets](https://mlsanity.com/paper/1810.05165/) (2018)
- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [A critical analysis of self-supervision, or what we can learn from a single image](https://mlsanity.com/paper/1904.13132/) (2019)
- [Particle Cloud Generation with Message Passing Generative Adversarial Networks](https://mlsanity.com/paper/2106.11535/) (2021)
- [Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models](https://mlsanity.com/paper/2401.13537/) (2024)
- [OmniJet-α: The first cross-task foundation model for particle physics](https://mlsanity.com/paper/2403.05618/) (2024)
- [Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics](https://mlsanity.com/paper/2405.14806/) (2024)
- [A Lorentz-Equivariant Transformer for All of the LHC](https://mlsanity.com/paper/2411.00446/) (2024)
- [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://mlsanity.com/paper/2307.16506/) (2023)
- [Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models](https://mlsanity.com/paper/2403.07066/) (2024)
- [Is Tokenization Needed for Masked Particle Modelling?](https://mlsanity.com/paper/2409.12589/) (2024)
- [How to prepare your task head for finetuning](https://mlsanity.com/paper/2302.05779/) (2023)
- [The National Research Platform: Stretched, Multi-Tenant, Scientific Kubernetes Cluster](https://mlsanity.com/paper/2505.22864/) (2025)
- [HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture](https://mlsanity.com/paper/2502.03933/) (2025)
- [Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture](https://mlsanity.com/paper/2412.05333/) (2024)

## Built on by (most cited first, 6 of 6)

- [Enhancing next token prediction based pre-training for jet foundation models](https://mlsanity.com/paper/2512.04149/) (2025)
- [Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physics](https://mlsanity.com/paper/2512.01324/) (2025)
- [jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillation](https://mlsanity.com/paper/2601.11719/) (2026)
- [Panda Diplomacy: Foundation Model Pre-training across Particle Imaging Detectors for High Energy and Nuclear Physics](https://mlsanity.com/paper/2609.00611/) (2026)
- [Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decays](https://mlsanity.com/paper/2609.17726/) (2026)
- [Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHC](https://mlsanity.com/paper/2609.17738/) (2026)

## Abstract (arXiv)

A common challenge with supervised machine learning (ML) in high energy physics (HEP) is the reliance on simulations for labeled data, which can often mismodel the underlying collision or detector response. To help mitigate this problem of domain shift, we propose RINO (Renormalization Group Invariance with No Labels), a self-supervised learning approach that can instead pretrain models directly on collision data, learning embeddings invariant to renormalization group flow scales. In this work, we pretrain a transformer-based model on jets originating from quantum chromodynamic (QCD) interactions from the JetClass dataset, emulating real QCD-dominated experimental data, and then finetune on the JetNet dataset -- emulating simulations -- for the task of identifying jets originating from top quark decays. RINO demonstrates improved generalization from the JetNet training data to JetClass data compared to supervised training on JetNet from scratch, demonstrating the potential for RINO pretraining on real collision data followed by fine-tuning on small, high-quality MC datasets, to improve the robustness of ML models in HEP.